Literature DB >> 28251682

New algorithm for tensor contractions on multi-core CPUs, GPUs, and accelerators enables CCSD and EOM-CCSD calculations with over 1000 basis functions on a single compute node.

Ilya A Kaliman1, Anna I Krylov1.   

Abstract

A new hardware-agnostic contraction algorithm for tensors of arbitrary symmetry and sparsity is presented. The algorithm is implemented as a stand-alone open-source code libxm. This code is also integrated with general tensor library libtensor and with the Q-Chem quantum-chemistry package. An overview of the algorithm, its implementation, and benchmarks are presented. Similarly to other tensor software, the algorithm exploits efficient matrix multiplication libraries and assumes that tensors are stored in a block-tensor form. The distinguishing features of the algorithm are: (i) efficient repackaging of the individual blocks into large matrices and back, which affords efficient graphics processing unit (GPU)-enabled calculations without modifications of higher-level codes; (ii) fully asynchronous data transfer between disk storage and fast memory. The algorithm enables canonical all-electron coupled-cluster and equation-of-motion coupled-cluster calculations with single and double substitutions (CCSD and EOM-CCSD) with over 1000 basis functions on a single quad-GPU machine. We show that the algorithm exhibits predicted theoretical scaling for canonical CCSD calculations, O(N6 ), irrespective of the data size on disk.
© 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Keywords:  GPGPU; coupled-cluster; electronic structure; equation-of-motion coupled-cluster; many-body theories; tensor computations

Year:  2017        PMID: 28251682     DOI: 10.1002/jcc.24713

Source DB:  PubMed          Journal:  J Comput Chem        ISSN: 0192-8651            Impact factor:   3.376


  2 in total

1.  Optimization of the linear-scaling local natural orbital CCSD(T) method: Redundancy-free triples correction using Laplace transform.

Authors:  Péter R Nagy; Mihály Kállay
Journal:  J Chem Phys       Date:  2017-06-07       Impact factor: 3.488

2.  Accurate Reduced-Cost CCSD(T) Energies: Parallel Implementation, Benchmarks, and Large-Scale Applications.

Authors:  László Gyevi-Nagy; Mihály Kállay; Péter R Nagy
Journal:  J Chem Theory Comput       Date:  2021-01-05       Impact factor: 6.006

  2 in total

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